Abstract:

Modern society's growing adoption of electric vehicles (EVs) closely ties with the need of advanced battery management systems (BMS). This is even more the case when one factors in the increasing adoption of electric vehicles (EVs) into green transport systems. Current battery management systems (BMS) models still seem to overlook the myriad of alterations that takes place within a battery’s operation due to varying commanding and environmental parameters. The focus of this work is enhancing battery management for EVs using advanced machine learning methods, including but not limited to deep neural networks (DNNs), recurrent neural networks (RNNs), and reinforcement learning (RL). Incorporating analytical models based on real-time and predictive data significantly enhances the estimation of SOC, SOH, and RUL relative to other models, thus improving vehicle range and performance. In this work, we present a comparative analysis aimed at reducing the prediction error for estimating state variables and the battery lifespan within the framework of diverse training procedures. Moreover, we propose a new adaptive approach based on prediction and static energy management using neural networks. Our research shows that applying machine learning to handle the BMS of EVs provides a revolutionary degree of flexibility and innovation unlike traditional approaches.If used cleverly, these opportunities could improve the effectiveness and operational productivity of electric vehicles significantly.